Qdrant Loader
Builds searchable knowledge bases from multiple data sources by loading data into a Qdrant vector database with MCP server support.
About
Qdrant Loader is a comprehensive toolkit designed to streamline the process of building searchable knowledge bases. It facilitates the collection and vectorization of technical content from diverse sources, storing it within a Qdrant vector database. The included MCP server enhances LLM application integration, providing seamless RAG capabilities to tools like Cursor IDE, enabling more intelligent and context-aware search experiences.
Key Features
- Supports multiple data source connectors (Git, Confluence, JIRA, Local Files, Public Docs).
- 2 GitHub stars
- Offers file conversion support for PDF, Office documents, and images.
- Includes an MCP server for LLM integration and RAG capabilities.
- Provides hierarchy-aware and attachment-aware search functionalities.
- Enables semantic search with real-time query processing.
Use Cases
- Enhancing search functionality within Confluence and JIRA environments.
- Integrating RAG capabilities into development environments like Cursor IDE.
- Creating searchable knowledge bases for enterprise documentation.
Sponsored